A new indicator for the Kunming–Montreal Global Biodiversity Framework: Capturing non-monetary benefit data from access and benefit-sharing agreements
Bibliographic record
Abstract
The 2022 Kunming-Montreal Global Biodiversity Framework (KMGBF) moves international efforts to conserve biodiversity into a quantitative era. Fair and equitable benefit-sharing is one of the three objectives of the Convention on Biological Diversity, which means that to achieve the KMGBF, its parties will need to begin quantifying the benefits received from access and benefit-sharing (ABS). This mandate represents a big challenge as countries will need to begin to measure both monetary and non-monetary benefits from ABS agreements. Non-monetary benefits, in particular, can be more difficult to measure than monetary benefits, resulting in lower scientific understanding and integration of scientific results into national policy choices. In the present article, we propose a new methodology to deliver data to the KMGBF on non-monetary benefit-sharing indicators using scientific publications that cite ABS permits and put forth recommendations for improving the visibility of non-monetary benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.036 | 0.051 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".